Listening Like a Judge: A Music-Aware Framework for Automatic Singing Performance Evaluation
arXiv:2606.26451v1 Announce Type: cross Abstract: Automatic singing quality assessment (SQA) requires evaluating lyrical correctness and musical fidelity while handling expressive variations. However, existing systems largely rely on either acoustic cues or lyric transcriptions exclusively, limiting holistic performance evaluation. Furthermore, their integration is non-trivial due to challenges in robust singing transcription amid melisma, vibrato, and tempo elasticity. To this end, we propose MusicJudge, a modality-guided framework for automated SQA that performs block-aligned multimodal anal
The proliferation of generative AI for audio and music production necessitates more sophisticated methods for automated quality assessment, driving innovation in this field.
Advanced AI for nuanced performance evaluation could significantly impact content creation, talent discovery, and education in the music industry, shifting how quality is perceived and measured.
The development of multimodal AI frameworks like MusicJudge moves beyond siloed acoustic or transcribed analysis to provide more holistic and Human-like evaluations of singing performances.
- · AI developers
- · Music education platforms
- · Talent discovery platforms
- · Audio software companies
- · Monotonous music assessment methods
- · Systems focused on single-modal analysis
Improved automated music performance evaluation tools become available for widespread use.
This technology streamlines processes for music producers, educators, and talent scouts, reducing manual effort and bias.
AI-driven performance feedback might redefine standards of musical 'goodness' and influence artistic training methodologies globally.
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Read at arXiv cs.LG